Improved ORB-SLAM2 Mobile Robot Vision Algorithm Based on Multiple Feature Fusion

Abstract

Traditional wheeled robot vision algorithms suffer from low texture tracking failures. Therefore, this study proposes a vision improvement algorithm for mobile robots in view of multi feature fusion; This algorithm introduces line surface features and Manhattan Frame on the basis of traditional algorithms, and proposes an improved algorithm in view of multi-sensor fusion to improve tracking accuracy. The experiment shows that the average Root-mean-square deviation of the position of the improved mobile robot vision algorithm in view of multi feature fusion is 0.02 in nine data packets of the Tum dataset; The average Root-mean-square deviation of the position of the data packet successfully tracked by the traditional wheeled robot vision algorithm is 0.016; It improved the average accuracy by 11.11%, which is 31.03% higher than the average accuracy of the Manhattan wheeled robot vision algorithm. Compared to the multi feature fusion based vision improvement algorithm for mobile robots and the closed-loop detection based multi-sensor improvement algorithm, the accuracy of the closed-loop detection based multi-sensor improvement algorithm has increased by 0.655% and 10.47%, respectively. The outcomes indicate that the improved algorithm can improve the accuracy of mobile robot tracking, thereby expanding its application range.

Keywords

• Multi feature fusion
• mobile robots
• visual algorithms
• multi sensor fusion
• encoder